TransFG: A Fine-Grained Model for Knowledge Graph Embedding

2019 ◽  
pp. 455-466 ◽  
Author(s):  
Yaowei Yu ◽  
Zhuoming Xu ◽  
Yan Lv ◽  
Jian Li
Keyword(s):  
Graph Embedding ◽  
Knowledge Graph ◽  
Fine Grained

Fine-Grained Evaluation of Knowledge Graph Embedding Model in Knowledge Enhancement Downstream Tasks

2021 ◽  
Vol 25 ◽  
pp. 100218
Author(s):  
Yuxin Zhang ◽  
Bohan Li ◽  
Han Gao ◽  
Ye Ji ◽  
Han Yang ◽  
...  
Keyword(s):  
Graph Embedding ◽  
Knowledge Graph ◽  
Fine Grained ◽  
Knowledge Enhancement

Fine-Grained Evaluation of Knowledge Graph Embedding Models in Downstream Tasks

2020 ◽  
pp. 242-256
Author(s):  
Yuxin Zhang ◽  
Bohan Li ◽  
Han Gao ◽  
Ye Ji ◽  
Han Yang ◽  
...  
Keyword(s):  
Graph Embedding ◽  
Knowledge Graph ◽  
Fine Grained

A Transition-Based Knowledge Graph Embedding with Adapting New Entities

2016 ◽  
Author(s):  
A-Yeong Kim ◽  
◽  
Hee-Guen Yoon ◽  
Seong-Bae Park ◽  
Se-Young Park ◽  
...  
Keyword(s):  
Graph Embedding ◽  
Knowledge Graph

scholarly journals TransET: Knowledge Graph Embedding with Entity Types

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1407
Author(s):  
Peng Wang ◽  
Jing Zhou ◽  
Yuzhang Liu ◽  
Xingchen Zhou
Keyword(s):  
Link Prediction ◽  
State Of The Art ◽  
Score Function ◽  
Graph Embedding ◽  
Vector Spaces ◽  
Knowledge Graph ◽  
Semantic Features ◽  
Knowledge Graphs ◽  
Real World Datasets ◽  
Low Dimensional

Knowledge graph embedding aims to embed entities and relations into low-dimensional vector spaces. Most existing methods only focus on triple facts in knowledge graphs. In addition, models based on translation or distance measurement cannot fully represent complex relations. As well-constructed prior knowledge, entity types can be employed to learn the representations of entities and relations. In this paper, we propose a novel knowledge graph embedding model named TransET, which takes advantage of entity types to learn more semantic features. More specifically, circle convolution based on the embeddings of entity and entity types is utilized to map head entity and tail entity to type-specific representations, then translation-based score function is used to learn the presentation triples. We evaluated our model on real-world datasets with two benchmark tasks of link prediction and triple classification. Experimental results demonstrate that it outperforms state-of-the-art models in most cases.

A Knowledge Graph Embedding Approach for Metaphor Processing

2021 ◽  
Vol 29 ◽  
pp. 406-420
Author(s):  
Wei Song ◽  
Jingjin Guo ◽  
Ruiji Fu ◽  
Ting Liu ◽  
Lizhen Liu
Keyword(s):  
Graph Embedding ◽  
Knowledge Graph ◽  
Metaphor Processing

scholarly journals MöbiusE: Knowledge Graph Embedding on Möbius ring

2021 ◽  
pp. 107181
Author(s):  
Yao Chen ◽  
Jiangang Liu ◽  
Zhe Zhang ◽  
Shiping Wen ◽  
Wenjun Xiong
Keyword(s):  
Graph Embedding ◽  
Knowledge Graph

Hierarchical-Aware Relation Rotational Knowledge Graph Embedding For Link Prediction

2021 ◽  
Author(s):  
Shensi Wang ◽  
Kun Fu ◽  
Xian Sun ◽  
Zequn Zhang ◽  
Shuchao Li ◽  
...  
Keyword(s):  
Link Prediction ◽  
Graph Embedding ◽  
Knowledge Graph
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